# Introduction to Statistical Analysis

Introduction to Statistical Analysis
When doing any kind of research, the experts must always collect data and look at it with the best tools for research analysis. Statistical analysis is the most common way to look at study data, but there are other tools that can be used as well. Statistical analysis is the process of collecting and interpreting study data to find patterns in the variables used in the research. Marso et al. (2016) did research on liraglitude and cardiovascular effects in type 2 diabetes. They used different statistical methods to look at the data, and this is what the study will look at.

Methods of Statistics

Researchers used different ways to look at the data they collected during the study. Graphing is the first type of statistical method that the experts used. Most of the time, graphs were used to show the time-to-event analysis, which was the first time someone died from a heart-related reason. From the study given in the research, it is clear that the researchers used the statistical method of graphs correctly. This is because graphs clearly show when the first death from different causes happened at different levels of risk. In the end, these graphs show an analysis of what the experts wanted to find out from their study. So, it was totally right to use graphs when studying the research data.

The next type of statistics used to look at research data is descriptive statistics. The P-Value of the research data was looked at in this way. Most of the time, the P-Value is used to show how important the data collected in a study is. If the P-Value is 0.05 or less, the results can only be considered significant. Based on the P-Value, some of the facts were important to study, while others were not important enough to study. Researchers were able to find the data that didn’t mean much by using P-Value as a way to describe it. P-Value was used correctly as a statistical method for analyzing data, and

thoroughly.

The piece is authentically easy to read. The piece is written in the right order and with the right words. There are neither long nor short words. They are very well put together, so people can quickly understand what the study is about. Not only was the study report easy to read, but the data was also correct as it was collected. The study gave the researchers the kind of information they were looking for. The validity of the study was checked using diachronic dependability, which means that the collected data were looked at over time and then used to analyze the study. So, the report was written in a way that was easy to understand and was correct and full for its purpose.

On the other hand, the study did not use different statistical methods, such as sampling or center and spread measures. But because of how the researchers did their work, the report as written is full.

Reference

Marso, S. P., Daniels, G. H., Brown-Frandsen, K., Kristensen, P., Mann, J. F., Nauck, M. A., … & Steinberg, W. M. (2016). Liraglutide and cardiovascular outcomes in type 2 diabetes. opens in the new window New England Journal of Medicine, 375(4), 311-322.